Symbolic rule extraction with a scaled conjugate gradient version of CLARION

Abstract

This paper presents a hybrid intelligent system made up of two modules. The bottom sub-symbolic module is a multi-layer feed-forward neural network trained by a modified Q-learning methodology that employs the scaled conjugate gradient algorithm. The top module is a symbolic system (implemented with a neural network built on-line) where rules are extracted from the bottom module during training, in a fashion similar to the CLARION system. The two modules augment each other in an effort to obtain a better performance than both of the modules acting alone in solving a problem. The originality of this work lies in the use of the advanced scaled conjugate learning algorithm in such a hybrid system. It is expected that the use of this algorithm would provide significant improvements in the performance of the overall system and also make it less dependent on user-selected parameters. This paper emphasises the implementation details, since the system is currently under development, rather that concrete experimental results.